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The paper compares two ways to make image tokenizers variable-length: patch-derived tokens that keep a spatial grid, and register tokens with no fixed layout. Patch-derived tokens reconstruct better at extreme compression, while register tokens generate better, but representation alignment with pretrained vision encoders closes much of that gap. For readers building generative image pipelines, the practical trade-off is between reconstruction fidelity, generation quality, and how much spatial structure the latents keep.
Patch-Derived or Register Tokens? Comparing Variable-Length Image Tokenizers
arXiv:2610.33540v1 Announce Type: cross Abstract: Image tokenizers convert pixels into compact latent sequences for efficient generative modeling. Most approaches arrange latents on a downsampled two-dimensional grid, tying sequence length to image resolution. We compare two variable-length strategies: computationally efficient patch-derived tokens, which inherit explicit spatial organization, and more demanding register tokens, which have no predefined spatial layout. Our reconstruction results reveal that patch-derived tokens perform substantially better under extreme compression, whereas register tokens achieve slightly better reconstruction quality at intermediate budgets. At high token budgets, both approaches achieve similar reconstruction quality. For downstream generative tasks, register tokens outperform patch-derived tokens. Adding representation alignment with external pretrained visual encoders substantially improves the generative performance of patch-derived tokens, allowing them to match or outperform register tokens depending on the token budget. We further find that both approaches learn spatially localized representations. Patch-derived tokens largely retain their explicitly imposed spatial organization, while register tokens develop an implicit two-dimensional spatial structure despite having no predefined layout. Representation alignment reduces this spatial organizational constraint, enabling individual tokens to exert a stronger non-local influence.
Title:Patch-Derived or Register Tokens? Comparing Variable-Length Image Tokenizers
Abstract:Image tokenizers convert pixels into compact latent sequences for efficient generative modeling. Most approaches arrange latents on a downsampled two-dimensional grid, tying sequence length to image resolution. We compare two variable-length strategies: computationally efficient patch-derived tokens, which inherit explicit spatial organization, and more demanding register tokens, which have no predefined spatial layout. Our reconstruction results reveal that patch-derived tokens perform substantially better under extreme compression, whereas register tokens achieve slightly better reconstruction quality at intermediate budgets. At high token budgets, both approaches achieve similar reconstruction quality. For downstream generative tasks, register tokens outperform patch-derived tokens. Adding representation alignment with external pretrained visual encoders substantially improves the generative performance of patch-derived tokens, allowing them to match or outperform register tokens depending on the token budget. We further find that both approaches learn spatially localized representations. Patch-derived tokens largely retain their explicitly imposed spatial organization, while register tokens develop an implicit two-dimensional spatial structure despite having no predefined layout. Representation alignment reduces this spatial organizational constraint, enabling individual tokens to exert a stronger non-local influence.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2610.33540 [cs.CV]
(or arXiv:2610.33540v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.33540 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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